Gustavo Díaz
McMaster University
diazg2@mcmaster.ca
@gusvalo
Verónica Pérez Bentancur
Universidad de la República veronica.perez@cienciassociales.edu.uy
@veroperezben
Ines Fynn
Pontificia Universidad Católica de Chile
ifynn@uc.cl
@ifynn_
Lucía Tiscornia
University College Dublin
lucia.tiscornia@ucd.ie
@tiscornia21
Slides: gustavodiaz.org/talk
Social scientists care about sensitive issues
Asking about them directly leads to misreporting
Solution: Indirect questioning techniques
List experiments popular in political science
Now I am going to read you things that make people angry or upset
After I read them all, just tell me HOW MANY of them upset you
I don’t want to know which ones, just tell me HOW MANY
I don’t want to know which ones, just tell me HOW MANY
I don’t want to know which ones, just tell me HOW MANY
Negatively correlated items (Glynn 2013)
Covariate adjustment (Blair and Imai 2012)
Auxiliary information (Chou 2020)
Double list experiments (Droitcour et al 1991)
Combine with direct questions (Aronow et al 2015)
Negatively correlated items (Glynn 2013)
Covariate adjustment (Blair and Imai 2012)
Auxiliary information (Chou 2020)
Double list experiments (Droitcour et al 1991)
Combine with direct questions (Aronow et al 2015)
\[ \hat{\mu} = \overline{Y} - (1 - \overline{Y}) (\overline{V}_{1,0} - \overline{V}_{0,0}) \]
\(\overline{Y}\): Proportion confess in direct question
\((\overline{V}_{1,0} - \overline{V}_{0,0})\): List experiment estimate among not confessing
Can’t always include direct questions
Combining with other indirect questions needs altered designs or extra modeling assumptions (e.g. Blair, Imai, and Lyall 2014)
How many people do you know,
How many people do you know, who also know you,
How many people do you know, who also know you, with whom you have interacted in the last year
How many people do you know, who also know you, with whom you have interacted in the last year in person, by phone, or any other channel.
Assumption
If someone knows an unusually large number of people with sensitive item, then they are likely to hold the sensitive item too.
\[ \begin{align*} y_{ik} \sim \text{negative-binomial}( & \text{mean} = e^{\alpha_i + \beta_k},\\ & \text{overdispersion} = \omega_k) \end{align*} \]
\(y_{ik}\): Degree of group \(k\) for person \(i\)
\(\alpha_i\): Expected degree of person \(i\) (logged)
\(\beta_k\): Expected degrees of group \(k\) (logged)
\(\omega_k\): Controls variance in propensity to know someone from group \(k\)
Fit with MLE in two steps (Personal network, sensitive group network)
Focus on standardized residuals:
\[ r_{ik} = \sqrt{y_{ik}} - \sqrt{e \alpha_i + \beta_k} \]
Low crime, but embedded
Even here criminal organizations replace government
Fieldwork: Interactions are sensitive topic
Goal: Document extent of exposure to criminal governance strategies (positive, negative)
(N = )